Probability Collectives Multi-Agent Systems: A Study of Robustness in Search

We present a robustness study of the search of Probability Collectives Multi-agent Systems (PCMAS) for optimization problems. This framework for distributed optimization is deeply connected with both game theory and statistical physics. In contrast to traditional biologically-inspired algorithms, Probability-Collectives (PC) based methods do not update populations of solutions; instead, they update an explicitly parameterized probability distribution p over the space of solutions by a collective of agents. That updating of p arises as the optimization of a functional of p. The functional is chosen so that any p that optimizes it should be p peaked about good solutions. By comparing with genetic algorithms, we show that the PCMAS method appeared superior to the GA method in initial rate of decent, long term performance as well as the robustness of the search on complex optimization problems.